Abstract
Due to the growing penetration of renewable energies (REs) in integrated energy system (IES), it is imperative to assess and reduce the negative impacts caused by the uncertain REs. In this paper, an unscented transformation-based mean-standard (UT-MS) deviation model is proposed for the stochastic optimization of cost-risk for IES operation considering wind and solar power correlated. The unscented transformation (UT) sampling method is adopted to characterize the uncertainties of wind and solar power considering the correlated relationship between them. Based on the UT, a mean-standard (MS) deviation model is formulated to depict the trade-off between the cost and risk of stochastic optimization for the IES optimal operation problem. Then the UT-MS model is tackled by a multi-objective group search optimizer with adaptive covariance and Lévy flights embedded with a multiple constraints handling technique (MGSO-ACL-CHT) to ensure the feasibility of Perato-optimal solutions. Furthermore, a decision making method, improve entropy weight (IEW), is developed to select a final operation point from the set of Perato-optimal solutions. In order to verify the feasibility and efficiency of the proposed UT-MS model in dealing with the uncertainties of correlative wind and solar power, simulation studies are conducted on a test IES. Simulation results show that the UT-MS model is capable of handling the uncertainties of correlative wind and solar power within much less samples and less computational burden. Moreover, the MGSO-ACL-CHT and IEW are also demonstrated to be effective in solving the multi-objective UT-MS model of the IES optimal operation problem.
Highlights
IntroductionDue to the fast depletion and severe pollution of fossil fuels, there is a massive stimulation to integrate the renewable energies (REs) such as wind and solar into integrated energy system (IES) [1]
CrossCheck date: January 2019Received: May 2018 / Accepted: 25 January 2019 / Published online: 20 July 2019 Ó The Author(s) 2019 & Mengshi LIIn recent years, due to the fast depletion and severe pollution of fossil fuels, there is a massive stimulation to integrate the renewable energies (REs) such as wind and solar into integrated energy system (IES) [1]
Multi-objective evolutionary algorithms proposed in recent decades, such as the non-dominated sorting genetic algorithm-II (NSGA-II) [37], multi-objective particle swarm optimizer [38], multi-objective differential evolution algorithm, MGSO-ACL [30] can search for multiple solutions in parallel and are insensitive to the shape of the objective functions such as discontinuity, non-convexity, multiple modality, non-uniformity of the search space [39], and they have been successfully applied in power system
Summary
Due to the fast depletion and severe pollution of fossil fuels, there is a massive stimulation to integrate the renewable energies (REs) such as wind and solar into integrated energy system (IES) [1]. The growing penetration of wind and solar power imposes challenges to the reliability and efficiency of IES operation since these resources are neither schedulable nor fully predictable [2, 3] In this regard, it is necessary to consider the uncertainties aroused by REs and to minimize the stochastic impact in the IES optimal operation problem [4]. The rest of this paper is organized as follows: Section 2 formulates the uncertainty characterization of wind and solar power using the UT-MS model for the IES optimal operation problem.
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